
"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
SCREAMING_SNAKE_CASE			  =  {"""LayoutLMv2Config""", """LayoutLMv3Config"""}



@is_pipeline_test
class 	__a	(   unittest.TestCase						):
       UpperCamelCase_		:       Optional[int]				=      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       UpperCamelCase_		:       List[Any]				=      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              UpperCamelCase_		:       Any				=      {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              UpperCamelCase_		:       List[Any]				=      {
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }






       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		pipeline(
                  task="text-classification"							,   model="hf-internal-testing/tiny-random-distilbert"							,   framework="pt"      )

              UpperCamelCase       			=		text_classifier("This is great !"      )
              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": "LABEL_0", "score": 0.504}]      )

              UpperCamelCase       			=		text_classifier("This is great !"							,   top_k=2      )
              self.assertEqual(
                  nested_simplify(UpperCAmelCase_      )							,   [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]      )

              UpperCamelCase       			=		text_classifier(["This is great !", "This is bad"]							,   top_k=2      )
              self.assertEqual(
                  nested_simplify(UpperCAmelCase_      )							,   [
                      [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
                      [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
                  ]							,   )

              UpperCamelCase       			=		text_classifier("This is great !"							,   top_k=1      )
              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": "LABEL_0", "score": 0.504}]      )

              # Legacy behavior
              UpperCamelCase       			=		text_classifier("This is great !"							,   return_all_scores=UpperCAmelCase_      )
              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": "LABEL_0", "score": 0.504}]      )

              UpperCamelCase       			=		text_classifier("This is great !"							,   return_all_scores=UpperCAmelCase_      )
              self.assertEqual(
                  nested_simplify(UpperCAmelCase_      )							,   [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]      )

              UpperCamelCase       			=		text_classifier(["This is great !", "Something else"]							,   return_all_scores=UpperCAmelCase_      )
              self.assertEqual(
                  nested_simplify(UpperCAmelCase_      )							,   [
                      [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
                      [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
                  ]							,   )

              UpperCamelCase       			=		text_classifier(["This is great !", "Something else"]							,   return_all_scores=UpperCAmelCase_      )
              self.assertEqual(
                  nested_simplify(UpperCAmelCase_      )							,   [
                      {"label": "LABEL_0", "score": 0.504},
                      {"label": "LABEL_0", "score": 0.504},
                  ]							,   )






       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	str:







              """simple docstring"""
              import torch

              UpperCamelCase       			=		pipeline(
                  task="text-classification"							,   model="hf-internal-testing/tiny-random-distilbert"							,   framework="pt"							,   device=torch.device("cpu"      )							,   )

              UpperCamelCase       			=		text_classifier("This is great !"      )
              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": "LABEL_0", "score": 0.504}]      )






       @require_tf
       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		pipeline(
                  task="text-classification"							,   model="hf-internal-testing/tiny-random-distilbert"							,   framework="tf"      )

              UpperCamelCase       			=		text_classifier("This is great !"      )
              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": "LABEL_0", "score": 0.504}]      )






       @slow
       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		pipeline("text-classification"      )

              UpperCamelCase       			=		text_classifier("This is great !"      )
              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": "POSITIVE", "score": 1.0}]      )
              UpperCamelCase       			=		text_classifier("This is bad !"      )
              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": "NEGATIVE", "score": 1.0}]      )
              UpperCamelCase       			=		text_classifier("Birds are a type of animal"      )
              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": "POSITIVE", "score": 0.988}]      )






       @slow
       @require_tf
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		pipeline("text-classification"							,   framework="tf"      )

              UpperCamelCase       			=		text_classifier("This is great !"      )
              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": "POSITIVE", "score": 1.0}]      )
              UpperCamelCase       			=		text_classifier("This is bad !"      )
              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": "NEGATIVE", "score": 1.0}]      )
              UpperCamelCase       			=		text_classifier("Birds are a type of animal"      )
              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": "POSITIVE", "score": 0.988}]      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		TextClassificationPipeline(model=UpperCAmelCase_							,   tokenizer=UpperCAmelCase_      )
              return text_classifier, ["HuggingFace is in", "This is another test"]






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Optional[Any]      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		text_classifier.model
              # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
              UpperCamelCase       			=		"HuggingFace is in"
              UpperCamelCase       			=		text_classifier(UpperCAmelCase_      )

              self.assertEqual(nested_simplify(UpperCAmelCase_      )							,   [{"label": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      )}]      )
              self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()      )

              UpperCamelCase       			=		["HuggingFace is in ", "Paris is in France"]
              UpperCamelCase       			=		text_classifier(UpperCAmelCase_      )
              self.assertEqual(
                  nested_simplify(UpperCAmelCase_      )							,   [{"label": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      )}, {"label": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      )}]							,   )
              self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()      )
              self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()      )

              # Forcing to get all results with `top_k=None`
              # This is NOT the legacy format
              UpperCamelCase       			=		text_classifier(UpperCAmelCase_							,   top_k=UpperCAmelCase_      )
              UpperCamelCase       			=		len(model.config.idalabel.values()      )
              self.assertEqual(
                  nested_simplify(UpperCAmelCase_      )							,   [[{"label": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      )}] * N, [{"label": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      )}] * N]							,   )

              UpperCamelCase       			=		{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
              UpperCamelCase       			=		text_classifier(UpperCAmelCase_      )
              self.assertEqual(
                  nested_simplify(UpperCAmelCase_      )							,   {"label": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      )}							,   )
              self.assertTrue(outputs["label"] in model.config.idalabel.values()      )

              # This might be used a text pair, but tokenizer + pipe interaction
              # makes it hard to understand that it's not using the pair properly
              # https://github.com/huggingface/transformers/issues/17305
              # We disabled this usage instead as it was outputting wrong outputs.
              UpperCamelCase       			=		[["HuggingFace is in ", "Paris is in France"]]
              with self.assertRaises(UpperCAmelCase_      ):
                     text_classifier(UpperCAmelCase_      )

              # This used to be valid for doing text pairs
              # We're keeping it working because of backward compatibility
              UpperCamelCase       			=		text_classifier([[["HuggingFace is in ", "Paris is in France"]]]      )
              self.assertEqual(
                  nested_simplify(UpperCAmelCase_      )							,   [{"label": ANY(UpperCAmelCase_      ), "score": ANY(UpperCAmelCase_      )}]							,   )
              self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()      )







"""simple docstring"""


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=7					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		None
       if token is not None:
              UpperCamelCase       			=		{"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

       # The id of a workflow (not of a workflow run)
       UpperCamelCase       			=		"636036"

       UpperCamelCase       			=		F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
       # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
       url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"

       UpperCamelCase       			=		requests.get(UpperCAmelCase_		,   headers=UpperCAmelCase_					).json()

       return result["workflow_runs"]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Any:







       """simple docstring"""
       UpperCamelCase       			=		get_daily_ci_runs(UpperCAmelCase_					)
       UpperCamelCase       			=		None
       for workflow_run in workflow_runs:
              if workflow_run["status"] == "completed":
                     UpperCamelCase       			=		workflow_run["id"]
                     break

       return workflow_run_id



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		get_last_daily_ci_runs(UpperCAmelCase_					)
       if workflow_run_id is not None:
              UpperCamelCase       			=		get_artifacts_links(worflow_run_id=UpperCAmelCase_		,   token=UpperCAmelCase_					)
              for artifact_name in artifact_names:
                     if artifact_name in artifacts_links:
                            UpperCamelCase       			=		artifacts_links[artifact_name]
                            download_artifact(
                                artifact_name=UpperCAmelCase_		,   artifact_url=UpperCAmelCase_		,   output_dir=UpperCAmelCase_		,   token=UpperCAmelCase_					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       get_last_daily_ci_artifacts(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       UpperCamelCase       			=		{}
       for artifact_name in artifact_names:
              UpperCamelCase       			=		os.path.join(UpperCAmelCase_		,   F"{artifact_name}.zip"					)
              if os.path.isfile(UpperCAmelCase_					):
                     UpperCamelCase       			=		{}
                     with zipfile.ZipFile(UpperCAmelCase_					) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(UpperCAmelCase_					):
                                          # read the file
                                          with z.open(UpperCAmelCase_					) as f:
                                                 UpperCamelCase       			=		f.read().decode("UTF-8"					)

       return results





